Refactoring detection task

src/otx/algo/common/backbones/cspnext.py

@@ -10,7 +10,7 @@
 
 import math
 from functools import partial
-from typing import Callable, ClassVar
+from typing import Any, Callable, ClassVar
 
 from otx.algo.common.layers import SPPBottleneck
 from otx.algo.detection.layers import CSPLayer
@@ -22,7 +22,7 @@
 from torch.nn.modules.batchnorm import _BatchNorm
 
 
-class CSPNeXt(BaseModule):
+class CSPNeXtModule(BaseModule):
     """CSPNeXt backbone used in RTMDet.
 
     Args:
@@ -225,3 +225,43 @@ def forward(self, x: tuple[Tensor, ...]) -> tuple[Tensor, ...]:
             if i in self.out_indices:
                 outs.append(x)
         return tuple(outs)
+
+
+class CSPNeXt:
+    """CSPNeXt factory for detection."""
+
+    CSPNEXT_CFG: ClassVar[dict[str, Any]] = {
+        "rtmdet_tiny": {
+            "deepen_factor": 0.167,
+            "widen_factor": 0.375,
+            "normalization": nn.BatchNorm2d,
+            "activation": partial(nn.SiLU, inplace=True),
+        },
+        "rtmpose_tiny": {
+            "arch": "P5",
+            "expand_ratio": 0.5,
+            "deepen_factor": 0.167,
+            "widen_factor": 0.375,
+            "out_indices": (4,),
+            "channel_attention": True,
+            "normalization": nn.BatchNorm2d,
+            "activation": partial(nn.SiLU, inplace=True),
+        },
+        "rtmdet_inst_tiny": {
+            "arch": "P5",
+            "expand_ratio": 0.5,
+            "deepen_factor": 0.167,
+            "widen_factor": 0.375,
+            "channel_attention": True,
+            "normalization": nn.BatchNorm2d,
+            "activation": partial(nn.SiLU, inplace=True),
+        },
+    }
+
+    def __new__(cls, model_name: str) -> CSPNeXtModule:
+        """Constructor for CSPNeXt."""
+        if model_name not in cls.CSPNEXT_CFG:
+            msg = f"model type '{model_name}' is not supported"
+            raise KeyError(msg)
+
+        return CSPNeXtModule(**cls.CSPNEXT_CFG[model_name])

src/otx/algo/common/utils/coders/__init__.py

@@ -3,7 +3,8 @@
 #
 """Custom coder implementations."""
 
+from .base_bbox_coder import BaseBBoxCoder
 from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder
 from .distance_point_bbox_coder import DistancePointBBoxCoder
 
-__all__ = ["DeltaXYWHBBoxCoder", "DistancePointBBoxCoder"]
+__all__ = ["BaseBBoxCoder", "DeltaXYWHBBoxCoder", "DistancePointBBoxCoder"]

src/otx/algo/common/utils/coders/base_bbox_coder.py

@@ -0,0 +1,26 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+"""Base bounding box coder."""
+
+from abc import ABCMeta, abstractmethod
+
+from torch import Tensor
+
+
+class BaseBBoxCoder(metaclass=ABCMeta):
+    """Base class for bounding box coder."""
+
+    encode_size: int
+
+    @abstractmethod
+    def encode(self, *args, **kwargs) -> Tensor:
+        """Encode bounding boxes."""
+
+    @abstractmethod
+    def decode(self, *args, **kwargs) -> Tensor:
+        """Decode bounding boxes."""
+
+    @abstractmethod
+    def decode_export(self, *args, **kwargs) -> Tensor:
+        """Decode bounding boxes for export."""

src/otx/algo/common/utils/coders/delta_xywh_bbox_coder.py

@@ -13,8 +13,10 @@
 from otx.algo.detection.utils.utils import clip_bboxes
 from torch import Tensor
 
+from .base_bbox_coder import BaseBBoxCoder
 
-class DeltaXYWHBBoxCoder:
+
+class DeltaXYWHBBoxCoder(BaseBBoxCoder):
     """Delta XYWH BBox coder.
 
     Following the practice in `R-CNN <https://arxiv.org/abs/1311.2524>`_,

src/otx/algo/common/utils/coders/distance_point_bbox_coder.py

@@ -13,11 +13,13 @@
 from otx.algo.common.utils.utils import bbox2distance, distance2bbox
 from otx.algo.detection.utils.utils import distance2bbox_export
 
+from .base_bbox_coder import BaseBBoxCoder
+
 if TYPE_CHECKING:
     from torch import Tensor
 
 
-class DistancePointBBoxCoder:
+class DistancePointBBoxCoder(BaseBBoxCoder):
     """Distance Point BBox coder.
 
     This coder encodes gt bboxes (x1, y1, x2, y2) into (top, bottom, left,

src/otx/algo/common/utils/prior_generators/__init__.py

@@ -4,6 +4,7 @@
 """Anchor generators for detection task."""
 
 from .anchor_generator import AnchorGenerator, SSDAnchorGeneratorClustered
+from .base_prior_generator import BasePriorGenerator
 from .point_generator import MlvlPointGenerator
 
-__all__ = ["AnchorGenerator", "SSDAnchorGeneratorClustered", "MlvlPointGenerator"]
+__all__ = ["AnchorGenerator", "SSDAnchorGeneratorClustered", "BasePriorGenerator", "MlvlPointGenerator"]

src/otx/algo/common/utils/prior_generators/anchor_generator.py

@@ -15,8 +15,10 @@
 from torch import Tensor
 from torch.nn.modules.utils import _pair
 
+from .base_prior_generator import BasePriorGenerator
 
-class AnchorGenerator:
+
+class AnchorGenerator(BasePriorGenerator):
     """Standard anchor generator for 2D anchor-based detectors.
 
     # TODO (sungchul): change strides format from (w, h) to (h, w)
@@ -72,7 +74,7 @@ def __init__(
             raise ValueError(msg)
 
         # calculate base sizes of anchors
-        self.strides = [_pair(stride) for stride in strides]
+        self.strides: list[tuple[int, int]] = [_pair(stride) for stride in strides]
         self.base_sizes = [min(stride) for stride in self.strides] if base_sizes is None else base_sizes
 
         if scales is not None:

src/otx/algo/common/utils/prior_generators/base_prior_generator.py

@@ -0,0 +1,37 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+"""Base prior generator."""
+
+from __future__ import annotations
+
+from abc import ABCMeta, abstractmethod
+from typing import TYPE_CHECKING, Callable
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+
+class BasePriorGenerator(metaclass=ABCMeta):
+    """Base class for prior generator."""
+
+    strides: list[tuple[int, int]]
+    grid_anchors: Callable[..., list[Tensor]]
+
+    @property
+    @abstractmethod
+    def num_base_priors(self) -> list[int]:
+        """Return the number of priors (anchors/points) at a point on the feature grid."""
+
+    @property
+    @abstractmethod
+    def num_levels(self) -> int:
+        """int: number of feature levels that the generator will be applied."""
+
+    @abstractmethod
+    def grid_priors(self, *args, **kwargs) -> list[Tensor]:
+        """Generate grid anchors/points of multiple feature levels."""
+
+    @abstractmethod
+    def valid_flags(self, *args, **kwargs) -> list[Tensor]:
+        """Generate valid flags of anchors/points of multiple feature levels."""

src/otx/algo/common/utils/prior_generators/point_generator.py

@@ -15,10 +15,12 @@
 from torch import Tensor
 from torch.nn.modules.utils import _pair
 
+from .base_prior_generator import BasePriorGenerator
+
 DeviceType = Union[str, torch.device]
 
 
-class MlvlPointGenerator:
+class MlvlPointGenerator(BasePriorGenerator):
     """Standard points generator for multi-level (Mlvl) feature maps in 2D points-based detectors.
 
     # TODO (sungchul): change strides format from (w, h) to (h, w)
@@ -31,7 +33,7 @@ class MlvlPointGenerator:
     """
 
     def __init__(self, strides: list[int] | list[tuple[int, int]], offset: float = 0.5) -> None:
-        self.strides = [_pair(stride) for stride in strides]
+        self.strides: list[tuple[int, int]] = [_pair(stride) for stride in strides]
         self.offset = offset
 
     @property